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Novel Methods to Deal with Publication Biases: Secondary Analysis of Antidepressant Trials in the FDA Trial Registry Database and Related Journal Publications

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Novel Methods to Deal with Publication Biases: Secondary Analysis of Antidepressant Trials in the FDA Trial Registry Database and Related Journal Publications RESEARCH BMJ: first published as 10.1136/bmj.b2981 on 7 August 2009. Downloaded from Novel methods to deal with publication biases: secondary analysis of antidepressant trials in the FDA trial registry database and related journal publications Santiago G Moreno, research student,1 Alex J Sutton, professor of medical statistics,1 Erick H Turner, assistant professor,2 Keith R Abrams, professor of medical statistics,1 Nicola J Cooper, senior research fellow,1 Tom M Palmer, research associate,3 A E Ades, professor of public health science4 1Department of Health Sciences, ABSTRACT the term publication bias has been used historically to University of Leicester, Leicester Objective To assess the performance of novel contour refer to the suppression of whole studies based on (the LE1 7RH enhanced funnel plots and a regression based “ ” 2Department of Psychiatry, lack of) statistical significance or interest level, a Oregon Health and Science adjustment method to detect and adjust for publication range of mechanisms can distort the published litera- University, Portland Veterans biases. ture. These include, in addition to the suppression of Affairs Medical Center, Portland, Design Secondary analysis of a published systematic Oregon, USA whole studies, selective reporting of outcomes or sub- literature review. “ ” 3MRC Centre for Causal Analyses groups; data massaging, such as the selective exclu- in Translational Epidemiology, Data sources Placebo controlled trials of antidepressants sion of patients from the analysis; and biases regarding Department of Social Medicine, previously submitted to the US Food and Drug timelines.2 A good umbrella term for all these is disse- University of Bristol Administration (FDA) and matching journal publications. mination biases34; in keeping with common usage we 4Department of Community Based Methods Publication biases were identified using novel refer to them as publication biases. If such biases are Medicine, University of Bristol http://www.bmj.com/ Correspondence to: S G Moreno contour enhanced funnel plots, a regression based present, any decision making based on the literature [email protected] adjustment method, Egger’s test, and the trim and fill could be misleading,56 not least through obtaining method. Results were compared with a meta-analysis of 7 Cite this as: BMJ 2009;339:b2981 inflated clinical effects from meta-analysis. doi:10.1136/bmj.b2981 the gold standard data submitted to the FDA. The FDA dataset is assumed to be an unbiased (but Results Severe asymmetry was observed in the contour not the complete) body of evidence in the specialty of enhanced funnel plot that appeared to be heavily antidepressants and so is regarded a gold standard data influenced by the statistical significance of results, source owing to the legal requirements of submitting on 30 September 2021 by guest. Protected copyright. suggesting publication biases as the cause of the evidence in its entirety to the FDA and its careful mon- asymmetry. Applying the regression based adjustment itoring for deviations from protocol.8-10 A gold stan- method to the journal data produced a similar pooled dard dataset will not, however, be available in most effect to that observed by a meta-analysis of the FDA data. contexts. In the absence of a gold standard, meta-ana- Contrasting journal and FDA results suggested that, in lysts have had to rely on analytical methods to both addition to other deviations from study protocol, detect and adjust for publication biases. This has been switching from an intention to treat analysis to a per an active area of methodology development over the protocol one would contribute to the observed past decades, with much written on approaches to deal discrepancies between the journal and FDA results. with publication biases in a meta-analysis context.2 Conclusion Novel contour enhanced funnel plots and a These include graphical diagnostic approaches and regression based adjustment method worked formal statistical tests to detect the presence of publica- convincingly and might have an important part to play in tion bias, and statistical approaches to modify effect combating publication biases. sizes to adjust a meta-analysis estimate when the pre- sence of publication bias is suspected.2 While the per- INTRODUCTION formance of many of these methods has been In 2008 Turner et al published a study in the New Eng- evaluated using simulation studies, concerns remain land Journal of Medicine showing that the scientific jour- as to whether the simulations reflect real life situations nal literature on antidepressants was biased towards and therefore whether their perceived performance is “favourable” results.1 The authors compared the representative of what would happen if they were used results in journal based reports of trials with data on in practice. Understandably this has led to caution in the corresponding trials submitted to the US Food the use of the methods, particularly for those that adjust and Drug Administration (FDA) when applying for effect sizes for publication biases6; but ultimately this is licensing. The discrepancies observed in the journal what is required for rational decision making if publi- based reports were due to publication biases. Although cation biases exist. BMJ | ONLINE FIRST | bmj.com page 1 of 7 RESEARCH We consider what we believe are currently the best compared with 51% according to the FDA. Data for BMJ: first published as 10.1136/bmj.b2981 on 7 August 2009. Downloaded from methods for identifying and adjusting for publication the analysis were extracted from the previous paper biases—both of which have been described only (table C in the appendix),1 in which two studies were recently. Specifically, we consider a funnel plot (a scat- combined, making a total of 73 studies in our assess- ter plot of effect size versus associated standard error) ment. enhanced by contours separating areas of statistical sig- nificance from non-significance.11 These contours help Analysis distinguish publication biases from other factors that We applied two novel methods to the journal dataset: lead to asymmetry in the funnel plot. The method the contour enhanced funnel plot11 16 to detect publica- used to adjust a meta-analysis for publication bias is tion biases, and a regression based adjustment based on a regression line fitted to the funnel plot.12 method12 to adjust for them. For completeness and The adjusted effect size is obtained by extrapolating comparison we also applied to the dataset the most the regression line to predict the effect size that would established and commonly used methods to deal with be seen in a hypothetical study of infinite size—that is, publication biases—namely, Egger’s regression test13 which has an effect size with zero associated standard for detecting bias, and the trim and fill adjustment error. For comparison and completeness we consider method (fixed effects linear estimator).14 17-19 The trim established methods to deal with publication bias. and fill method is an iterative non-parametric techni- These are the regression based Egger’s test for funnel que that uses rank based data augmentation to adjust asymmetry,13 and the trim and fill method,14 which for publication bias by imputing studies estimated to be adjusts a meta-analysis for publication bias by imput- missing from the dataset. We use fixed effect models ing studies to rectify any asymmetry in the funnel plot. for the primary analysis in this paper; we also reana- The dataset from Turner et al provides a unique lysed the data using random effects models as a sensi- opportunity to evaluate the performance of these ana- tivity analysis. Stata v.9.2 was used for all the analyses. lytical methods against a gold standard. We present the results of applying the diagnostic and adjustment meth- Contour enhanced funnel plots ods to the journal published results and compare the In its simplest form a funnel plot is a scatter plot of findings with those obtained through (gold standard) study effect sizes (x axis) against their estimated stan- analysis of the data submitted to the FDA. dard errors (y axis).20 When no bias is present such a plot should be symmetrical, with increasing variability METHODS in effect sizes being observed in the less precise studies http://www.bmj.com/ A full description of the dataset, how it was obtained, towards the bottom of the plot, producing a funnel and the references to the trials associated with it have shape. Asymmetry in this plot may indicate that pub- been published previously.1 Briefly, Turner et al iden- lication biases are present through the lack of observed tified the cohort of all phase II and phase III short term data points in a region of the plot.20 Asymmetry alone double blind placebo controlled trials used for the does not necessarily imply publication biases exist, licensing of antidepressant drugs between 1987 and however, since alternative explanations for the asym- 21 2004 by the FDA. Seventy four trials registered with metry may be present. For example, confounding on 30 September 2021 by guest. Protected copyright. the FDA and involving 12 drugs and 12 564 patients factors (that is, any unmeasured variable associated were identified. To compare drug efficacy reported by with both study precision and effect size) may distort the published literature with that of the FDA gold stan- the appearance of the plot. It has been observed that dard, Turner et al collected data on the primary out- certain aspects of trial quality may influence the esti- come from both sources. Once the primary outcome mates of effect size,22-25 and empirical evidence sug- data were extracted from the FDA trial registry, they gests that small studies are, on average, of lower searched the published scientific literature for publica- quality and this could induce asymmetry on a funnel tions matching the same trials.
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